When does class start/end?
Classes begin promptly at 9:00 am, and typically end at 5:00 pm.
This IBM Web-Based Training (WBT) is Self-Paced and includes: - Instructional content available online for duration of course - Visuals without hands-on lab exercises This course goes through the...Read More
This IBM Web-Based Training (WBT) is Self-Paced and includes:
- Instructional content available online for duration of course
- Visuals without hands-on lab exercises
This course goes through the stages of a data science project from importing data to deployment, using services in Watson Studio and Watson Machine Learning for Cloud Pak for Data.
- Introduction to Watson Studio and Watson Machine Learning for Cloud Pak for Data- Work with analytics projects- Import data- Prepare data for modeling with Data Refinery- Automate building supervised models with AutoAI experiment- Work with notebooks- Deploy Watson Machine Learning models
Clients who want to use the data science capabilities on Cloud Pak for Data or those who want to learn more about data science
Knowledge of your business requirements
Introduction to Watson Studio and Watson Machine Learning for Cloud Pak for Data- Describe the IBM Cloud Pak for Data platform and AI- Describe the four rungs in the ladder to AI- Describe the personas on the platform- Describe how to collaborate on the platform- Describe the CRISP-DM methodology Work with analytics projects- Describe analytics projects- Create analytics projects- Leverage industry accelerators Import data- Identify key concepts in working with data- Describe correct column types- Add local files to the project- Created connections- Add connected data sets to the project Prepare data for modeling with Data Refinery- Identify three tasks in preparing data for modeling - Describe the capabilities of Data Refinery- Describe steps, flows, and jobs- Join data- Profile data- Visualize data Automate building supervised models with AutoAI experiment- Describe when AutoAI experiment can be used- Describe the importance of column types- Describe how the best model is identified- Describe pipelines- Save AutoAI experiment pipelines to the project- Explain evaluation measures Work with notebooks- Work with notebooks- Load data into a notebook- Prepare data for modeling- Build machine learning models- Save machine learning models to the project Deploy Watson Machine Learning models- Identify Watson Machine Learning models- Describe deployment spaces- Create deployment spaces- Describe model deployment options- Create deployments- Test deployments
Save up to $250-$2500 Use Promo Code: SurfBoard
View Details Register by September 6, 2019